Deskripsi Pekerjaan
At Apex Neural Systems, we are building the next generation of autonomous decision-making infrastructure. We are looking for a Senior Machine Learning Engineer to lead the design, training, and deployment of large-scale foundation models that power our core product suite. You will operate at the intersection of cutting-edge research and high-throughput production engineering, solving complex problems in distributed training, model optimization, and MLOps architecture.
This is a high-impact role where your work directly shapes the intelligence layer of our platform. You will collaborate with a world-class team of researchers and engineers in a culture that values technical rigor, rapid iteration, and intellectual honesty. We offer a competitive equity package, comprehensive healthcare, and a flexible hybrid work policy anchored in our vibrant Financial District office.
Tanggung Jawab
- Architect and implement scalable ML pipelines for training, evaluation, and deployment of LLMs and multimodal models.
- Optimize distributed training workflows on GPU clusters (Kubernetes/Slurm) utilizing FSDP, DeepSpeed, or Megatron-LM.
- Lead model compression, quantization, and compilation (TensorRT, ONNX, Triton) for low-latency inference at scale.
- Design and maintain robust MLOps infrastructure: feature stores, experiment tracking, CI/CD for models, and automated monitoring/retraining loops.
- Collaborate with Product and Research to translate ambiguous business requirements into measurable ML tasks and evaluation benchmarks.
- Mentor junior engineers, establish coding standards, and drive technical strategy for the ML platform team.
- Conduct rigorous offline/online evaluation (A/B testing, shadow deployments) to ensure model quality and reliability.
Kualifikasi
- M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or equivalent practical experience.
- 5+ years of industry experience building and shipping production ML systems at scale.
- Expert proficiency in Python, PyTorch/JAX, and deep learning fundamentals (Transformers, Diffusion, RLHF).
- Proven track record with distributed training (multi-node/multi-GPU) and high-performance computing environments.
- Strong software engineering fundamentals: Linux internals, networking, containerization (Docker/K8s), and CI/CD.
- Experience serving models in production (TensorRT-LLM, vLLM, Triton, BentoML) with strict SLA requirements.
- Excellent communication skills; ability to distill complex technical concepts for cross-functional stakeholders.